Abstract

Fully Bayesian approaches are not commonly implemented for population pharmacokinetic (PK) modeling. In this paper, we evaluate the use of Stan with R and Torsten for population PK modeling of somatrogon, a recombinant long-acting growth hormone approved for the treatment of growth hormone deficiency. As a software for Bayesian inference, Stan provides an easy way to conduct MCMC sampling for a wide range of models with efficient sampling algorithms, and there are several diagnostic tools to evaluate the MCMC convergence and other potential issues. Three different sets of priors were evaluated for estimation and prediction: a weakly informative uniform set, a moderately informative set, and a very informative set of priors. All three prior sets showed good performance and all chains mixed well. There were some minor differences in the final parameter posterior distributions while implementing different prior sets, but the posterior predictions covered the observations nicely, not only …

We published our work in Clinical Pharmacology and Drug Development.

Link to article: https://doi.org/10.1002/cpdd.1465
Authors: Luke Fostvedt, Jian Liu, Xiaoxing Wang, Yinhua Li, Jillian Johnson, Linda Wood,
Martin Dowty, Bimal Malhotra, Hernan Valdez, Timothy Nicholas, and Wei Xue

Abstract

Abrocitinib is a selective Janus kinase 1 inhibitor approved for the treatment of atopic dermatitis. It is metabolized primarily by cytochrome P450 (CYP) 2C19 (approximately 53%) and CYP2C9 (approximately 30%), which form 2 active metabolites. The pharmacologic activity of abrocitinib is attributable to the unbound exposures of abrocitinib and those metabolites with active moiety area under the plasma concentration–time curve (AUC) considered the best measure of the total pharmacological effect. The effect of CYP2C19 and/or CYP2C9 genotypes on abrocitinib and active moiety exposures were evaluated using a meta-analysis of the noncompartmental estimates of exposure pooled from 10 clinical studies. A linear mixed-effects model was developed on the basis of the power …

Abstract

PoissonERM is an R package used to conduct exposure–response (ER) analysis on binary outcomes for establishing the relationship between exposure and the occurrence of adverse events (AE). While Poisson regression could be implemented with glm(), PoissonERM provides a simple way to semi-automate the entire analysis and generate an abbreviated report as an R markdown (Rmd) file that includes the essential analysis details with brief conclusions. PoissonERM processes the provided data set using the information from the user’s control script and generates summary tables/figures for the exposure metrics, covariates, and event counts of each endpoint (each type of AE). After checking the incidence rate of each AE, the correlation, and missing values in each covariate, an exposure–response model is developed for each endpoint based on the provided specifications. PoissonERM has the flexibility to incorporate and compare multiple scale transformations in its modeling. The best exposure metric is selected based on …

Abrocitinib is an approved treatment for people with moderate or severe atopic dermatitis. Abrocitinib tablets are available in two doses (100 and 200 mg) and are taken by mouth once daily. Some people with atopic dermatitis who are taking abrocitinib 100 mg may need to increase the dose to 200 mg to get adequate symptom relief. We studied whether people with atopic dermatitis who did or did not experience clear skin or itch relief after taking abrocitinib 100 mg for 4 weeks are likely or not likely to experience relief after 12 weeks of treatment. We also defined the level of response after 4 weeks of treatment that best differentiates people who did or did not experience symptom relief, and we identified who might benefit from increasing the abrocitinib dose from 100 to 200 mg. We found that people with atopic dermatitis who had symptom relief after 4 weeks of …

We published the tumor modeling we did for dacomitinib in Clinical Pharmacology and Therapeutics: Pharmacometrics and Systems Pharmacology.

Link to Paper: doi:10.1002/psp4.12841

Fostvedt LK, Nickens DJ, Tan W, Parivar K. Tumor growth inhibition modeling to support the starting dose for dacomitinib. CPT Pharmacometrics Syst Pharmacol. 2022;11:1256-1267.

Abstract:

Dacomitinib is a second-generation, irreversible EGFR tyrosine kinase inhibitor for first-line treatment of patients with metastatic non-small cell lung cancer and EGFR-activating mutations. A high rate of dose reductions in the pivotal trial led to an observed inverse exposure-response (ER) relationship with the primary end points. Three ER models were developed to determine if the starting dose from the pivotal trial, 45 mg once daily (q.d.) dose, is appropriate: a longitudinal logistic regression model for adverse event-related dose changes, a Claret tumor growth inhibition (TGI) model, and a Cox model for progression-free survival (PFS) based on the TGI model predictions. This analysis included …